Building Trust and Explainability into Chatbots: the Partena Ziekenfonds Business Case, presented by Karel Kremer, CTO of Oswald, at the Trustworthy and Ethical AI Conference on Feb 13th, 2020
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Chatbots and conversational interfaces are taking over customer service departments by storm. In many companies, they provide first-line support to customers. Based on the Partena Ziekenfonds business case, Karel Kremer shares a few critical success factors...
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Building Trust and Explainability into Chatbots: the Partena Ziekenfonds Business Case, presented by Karel Kremer, CTO of Oswald, at the Trustworthy and Ethical AI Conference on Feb 13th, 2020
- 2. OSWALD, an inside view PARTENA CASE TRAIN INTERFACE BOT BRAIN CONFUSION MATRIX TEST CASES STATISTICS HUMAN TAKEOVER
- 3. ”I want to go to Paris in two weeks” CHATBOTS OSWALD Real quick! Intent + Entities + Context = Response I want to go to Paris in two weeksDestination:Paris Date:2020-02-27 intent: Plan Trip Context: - name: John Doe - Means: Flight - Location: LA, CA Ok John, here are some flights you might like…
- 7. PARTENA CASE OSWALD Disclaimer Sorry no data on Partena in this presenation! But we will show you the tools they have available J
- 8. PARTENA CASE OSWALD Before Call center with information in large CMS system. Customers with a question immediately end up with the helpdesk Helpdesk manually takes customers through the flows defined in CMS
- 9. PARTENA CASE OSWALD Then Right. Let’s automate! Synchronize CMS with Oswald! Works! Gaining understanding of the user. But little control, different medium, …
- 10. PARTENA CASE OSWALD Now In control. Clear flows, understand why the chatbot behaves as it does. Integrate with backend systems. And adapt to the user.
- 11. TOOLSUITE
- 12. TRAIN INTERFACE OSWALD Try out and unanswered Enter some text here and see how the bot understands your input. Also find input sent by your users that hasn’t been handled as it should. Filter by problem type, intent score, archive status View brain and takeover
- 14. BOT BRAIN OSWALD Have a look at how Oswald THINKS! What are the intent and the entities it understood? What was in context? How did the response it gave change that? Any tags in the session? Which response did it give?
- 15. BOT BRAIN OSWALD
- 16. BOT BRAIN OSWALD
- 17. BOT BRAIN OSWALD
- 18. BOT BRAIN OSWALD
- 19. CONFUSION MATRIX OSWALD What confuses your bot? Train your bot on 80% of training sentences Use the other 20% as input and examine the results. This can be a measure for how well your bot will perform on new input. Which intents are too much alike, which simply need more training sentences?
- 22. TEST CASES OSWALD Trust that your chatbot handles changes When retraining intents When adding rules to scenarios When modifying critical responses
- 24. STATISTICS OSWALD How do your users actually use your chatbot? What intents are used? What scenarios? What tags? Load?
- 26. HUMAN TAKEOVER OSWALD When your bot needs a hand View and filter conversations Take over from the bot when things go wrong How did the user get here?
- 28. HYBRID CLOUD / ON PREM OSWALD
- 29. TRUST OSWALD Make humans feel safe from the scary robot… Bot vs. Human Ask consent Anonymize your data Send transcriptions Forget Me
- 30. DEMO OSWALD
- 31. Thank you OSWALD